Switching Trackers for Effective Sensor Fusion in Advanced Driver Assistance Systems

Modern cars utilise Advanced Driver Assistance Systems (ADAS) in several ways. In ADAS, the use of multiple sensors to gauge the environment surrounding the ego-vehicle offers numerous advantages, as fusing information from more than one sensor helps to provide highly reliable and error-free data. The fused data is typically then fed to a tracker algorithm, which helps to reduce noise and compensate for situations when received sensor data is temporarily absent or spurious, or to counter the offhand false positives and negatives. The performances of these constituent algorithms vary vastly under different scenarios. In this paper, we focus on the variation in the performance of tracker algorithms in sensor fusion due to the alteration in external conditions in different scenarios, and on the methods for countering that variation. We introduce a sensor fusion architecture, where the tracking algorithm is spontaneously switched to achieve the utmost performance under all scenarios. By employing a Real-time Traffic Density Estimation (RTDE) technique, we may understand whether the ego-vehicle is currently in dense or sparse traffic conditions. A highly dense traffic (or congested traffic) condition would mean that external circumstances are non-linear; similarly, sparse traffic conditions would mean that the probability of linear external conditions would be higher. We also employ a Traffic Sign Recognition (TSR) algorithm, which is able to monitor for construction zones, junctions, schools, and pedestrian crossings, thereby identifying areas which have a high probability of spontaneous, on-road occurrences. Based on the results received from the RTDE and TSR algorithms, we construct a logic which switches the tracker of the fusion architecture between an Extended Kalman Filter (for linear external scenarios) and an Unscented Kalman Filter (for non-linear scenarios). This ensures that the fusion model always uses the tracker that is best suited for its current needs, thereby yielding consistent accuracy across multiple external scenarios, compared to the fusion models that employ a fixed single tracker

Fusing Loop and GPS Probe Measurements to Estimate Freeway Density

In an age of ever-increasing penetration of GPS-enabled mobile devices, the potential of real-time "probe" location information for estimating the state of transportation networks is receiving increasing attention. Much work has been done on using probe data to estimate the current speed of vehicle traffic (or equivalently, trip travel time). While travel times are useful to individual drivers, the state variable for a large class of traffic models and control algorithms is vehicle density. Our goal is to use probe data to supplement traditional, fixed-location loop detector data for density estimation. To this end, we derive a method based on Rao-Blackwellized particle filters, a sequential Monte Carlo scheme. We present a simulation where we obtain a 30\% reduction in density mean absolute percentage error from fusing loop and probe data, vs. using loop data alone. We also present results using real data from a 19-mile freeway section in Los Angeles, California, where we obtain a 31\% reduction. In addition, our method's estimate when using only the real-world probe data, and no loop data, outperformed the estimate produced when only loop data were used (an 18\% reduction). These results demonstrate that probe data can be used for traffic density estimation

A machine learning approach to pedestrian detection for autonomous vehicles using High-Definition 3D Range Data

This article describes an automated sensor-based system to detect pedestrians in an autonomous vehicle application. Although the vehicle is equipped with a broad set of sensors, the article focuses on the processing of the information generated by a Velodyne HDL-64E LIDAR sensor. The cloud of points generated by the sensor (more than 1 million points per revolution) is processed to detect pedestrians, by selecting cubic shapes and applying machine vision and machine learning algorithms to the XY, XZ, and YZ projections of the points contained in the cube. The work relates an exhaustive analysis of the performance of three different machine learning algorithms: k-Nearest Neighbours (kNN), Naïve Bayes classifier (NBC), and Support Vector Machine (SVM). These algorithms have been trained with 1931 samples. The final performance of the method, measured a real traffic scenery, which contained 16 pedestrians and 469 samples of non-pedestrians, shows sensitivity (81.2%), accuracy (96.2%) and specificity (96.8%).This work was partially supported by ViSelTR (ref. TIN2012-39279) and cDrone (ref. TIN2013-45920-R) projects of the Spanish Government, and the “Research Programme for Groups of Scientific Excellence at Region of Murcia” of the Seneca Foundation (Agency for Science and Technology of the Region of Murcia—19895/GERM/15). 3D LIDAR has been funded by UPCA13-3E-1929 infrastructure projects of the Spanish Government. Diego Alonso wishes to thank the Spanish Ministerio de Educación, Cultura y Deporte, Subprograma Estatal de Movilidad, Plan Estatal de Investigación Científica y Técnica y de Innovación 2013–2016 for grant CAS14/00238

Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood Maps

Hyperspectral cameras can provide unique spectral signatures for consistently distinguishing materials that can be used to solve surveillance tasks. In this paper, we propose a novel real-time hyperspectral likelihood maps-aided tracking method (HLT) inspired by an adaptive hyperspectral sensor. A moving object tracking system generally consists of registration, object detection, and tracking modules. We focus on the target detection part and remove the necessity to build any offline classifiers and tune a large amount of hyperparameters, instead learning a generative target model in an online manner for hyperspectral channels ranging from visible to infrared wavelengths. The key idea is that, our adaptive fusion method can combine likelihood maps from multiple bands of hyperspectral imagery into one single more distinctive representation increasing the margin between mean value of foreground and background pixels in the fused map. Experimental results show that the HLT not only outperforms all established fusion methods but is on par with the current state-of-the-art hyperspectral target tracking frameworks.Comment: Accepted at the International Conference on Computer Vision and Pattern Recognition Workshops, 201